Overview

Brought to you by YData

Dataset statistics

Number of variables12
Number of observations2825
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory286.9 KiB
Average record size in memory104.0 B

Variable types

Numeric12

Alerts

avg_recency_days is highly overall correlated with frequencyHigh correlation
avg_ticket is highly overall correlated with u_basket_sizeHigh correlation
basket_size is highly overall correlated with gross_revenue and 1 other fieldsHigh correlation
frequency is highly overall correlated with avg_recency_daysHigh correlation
gross_revenue is highly overall correlated with basket_size and 3 other fieldsHigh correlation
qtde_invoices is highly overall correlated with gross_revenue and 2 other fieldsHigh correlation
qtde_itens is highly overall correlated with basket_size and 3 other fieldsHigh correlation
qtde_products is highly overall correlated with gross_revenue and 3 other fieldsHigh correlation
u_basket_size is highly overall correlated with avg_ticket and 1 other fieldsHigh correlation
avg_ticket is highly skewed (γ1 = 48.90445591)Skewed
frequency is highly skewed (γ1 = 22.8687173)Skewed
qtde_returns is highly skewed (γ1 = 50.55990893)Skewed
basket_size is highly skewed (γ1 = 45.1395063)Skewed
customer_id has unique valuesUnique
recency_days has 34 (1.2%) zerosZeros
avg_recency_days has 51 (1.8%) zerosZeros
qtde_returns has 1524 (53.9%) zerosZeros

Reproduction

Analysis started2025-10-31 13:11:10.082946
Analysis finished2025-10-31 13:11:24.681617
Duration14.6 seconds
Software versionydata-profiling vv4.17.0
Download configurationconfig.json

Variables

customer_id
Real number (ℝ)

Unique 

Distinct2825
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15299.886
Minimum12347
Maximum18287
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size44.1 KiB
2025-10-31T10:11:24.757671image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum12347
5-th percentile12626.2
Q113827
median15271
Q316801
95-th percentile17953.4
Maximum18287
Range5940
Interquartile range (IQR)2974

Descriptive statistics

Standard deviation1714.2532
Coefficient of variation (CV)0.11204352
Kurtosis-1.2048524
Mean15299.886
Median Absolute Deviation (MAD)1484
Skewness0.0021078867
Sum43222179
Variance2938664
MonotonicityNot monotonic
2025-10-31T10:11:24.862945image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
160001
 
< 0.1%
178501
 
< 0.1%
130471
 
< 0.1%
125831
 
< 0.1%
137481
 
< 0.1%
151001
 
< 0.1%
152911
 
< 0.1%
146881
 
< 0.1%
178091
 
< 0.1%
175021
 
< 0.1%
Other values (2815)2815
99.6%
ValueCountFrequency (%)
123471
< 0.1%
123481
< 0.1%
123521
< 0.1%
123561
< 0.1%
123581
< 0.1%
123591
< 0.1%
123601
< 0.1%
123621
< 0.1%
123641
< 0.1%
123701
< 0.1%
ValueCountFrequency (%)
182871
< 0.1%
182831
< 0.1%
182821
< 0.1%
182731
< 0.1%
182721
< 0.1%
182701
< 0.1%
182651
< 0.1%
182631
< 0.1%
182611
< 0.1%
182601
< 0.1%

gross_revenue
Real number (ℝ)

High correlation 

Distinct2809
Distinct (%)99.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2879.4906
Minimum36.56
Maximum279138.02
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size44.1 KiB
2025-10-31T10:11:24.961034image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum36.56
5-th percentile251.878
Q1614.66
median1142.99
Q32389.1
95-th percentile7538.114
Maximum279138.02
Range279101.46
Interquartile range (IQR)1774.44

Descriptive statistics

Standard deviation10856.745
Coefficient of variation (CV)3.77037
Kurtosis334.71234
Mean2879.4906
Median Absolute Deviation (MAD)683.75
Skewness16.301075
Sum8134560.9
Variance1.1786891 × 108
MonotonicityNot monotonic
2025-10-31T10:11:25.069657image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
598.22
 
0.1%
2053.022
 
0.1%
1078.962
 
0.1%
3312
 
0.1%
533.332
 
0.1%
734.942
 
0.1%
1314.452
 
0.1%
379.652
 
0.1%
178.962
 
0.1%
889.932
 
0.1%
Other values (2799)2805
99.3%
ValueCountFrequency (%)
36.561
< 0.1%
521
< 0.1%
52.21
< 0.1%
62.431
< 0.1%
68.841
< 0.1%
70.021
< 0.1%
77.41
< 0.1%
84.651
< 0.1%
90.31
< 0.1%
93.351
< 0.1%
ValueCountFrequency (%)
279138.021
< 0.1%
259657.31
< 0.1%
194550.791
< 0.1%
168472.51
< 0.1%
140450.721
< 0.1%
124564.531
< 0.1%
117379.631
< 0.1%
91062.381
< 0.1%
72882.091
< 0.1%
66653.561
< 0.1%

recency_days
Real number (ℝ)

Zeros 

Distinct257
Distinct (%)9.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean58.084956
Minimum0
Maximum373
Zeros34
Zeros (%)1.2%
Negative0
Negative (%)0.0%
Memory size44.1 KiB
2025-10-31T10:11:25.179420image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2
Q110
median29
Q374
95-th percentile215
Maximum373
Range373
Interquartile range (IQR)64

Descriptive statistics

Standard deviation70.211083
Coefficient of variation (CV)1.2087654
Kurtosis3.2630081
Mean58.084956
Median Absolute Deviation (MAD)24
Skewness1.8725457
Sum164090
Variance4929.5962
MonotonicityNot monotonic
2025-10-31T10:11:25.278184image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
199
 
3.5%
487
 
3.1%
286
 
3.0%
385
 
3.0%
876
 
2.7%
1069
 
2.4%
966
 
2.3%
765
 
2.3%
1762
 
2.2%
2256
 
2.0%
Other values (247)2074
73.4%
ValueCountFrequency (%)
034
 
1.2%
199
3.5%
286
3.0%
385
3.0%
487
3.1%
543
1.5%
765
2.3%
876
2.7%
966
2.3%
1069
2.4%
ValueCountFrequency (%)
3731
 
< 0.1%
3721
 
< 0.1%
3691
 
< 0.1%
3661
 
< 0.1%
3601
 
< 0.1%
3583
0.1%
3541
 
< 0.1%
3371
 
< 0.1%
3362
0.1%
3341
 
< 0.1%

qtde_invoices
Real number (ℝ)

High correlation 

Distinct55
Distinct (%)1.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.9823009
Minimum2
Maximum206
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size44.1 KiB
2025-10-31T10:11:25.371931image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile2
Q12
median4
Q36
95-th percentile17
Maximum206
Range204
Interquartile range (IQR)4

Descriptive statistics

Standard deviation9.0045848
Coefficient of variation (CV)1.5052043
Kurtosis186.42448
Mean5.9823009
Median Absolute Deviation (MAD)2
Skewness10.689372
Sum16900
Variance81.082548
MonotonicityNot monotonic
2025-10-31T10:11:25.479805image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2826
29.2%
3503
17.8%
4394
13.9%
5237
 
8.4%
6173
 
6.1%
7138
 
4.9%
898
 
3.5%
969
 
2.4%
1055
 
1.9%
1154
 
1.9%
Other values (45)278
 
9.8%
ValueCountFrequency (%)
2826
29.2%
3503
17.8%
4394
13.9%
5237
 
8.4%
6173
 
6.1%
7138
 
4.9%
898
 
3.5%
969
 
2.4%
1055
 
1.9%
1154
 
1.9%
ValueCountFrequency (%)
2061
< 0.1%
1991
< 0.1%
1241
< 0.1%
971
< 0.1%
912
0.1%
861
< 0.1%
721
< 0.1%
622
0.1%
601
< 0.1%
571
< 0.1%

qtde_itens
Real number (ℝ)

High correlation 

Distinct1654
Distinct (%)58.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1676.8634
Minimum2
Maximum196844
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size44.1 KiB
2025-10-31T10:11:25.570949image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile114.2
Q1323
median684
Q31469
95-th percentile4588.4
Maximum196844
Range196842
Interquartile range (IQR)1146

Descriptive statistics

Standard deviation6027.3154
Coefficient of variation (CV)3.5943987
Kurtosis445.02325
Mean1676.8634
Median Absolute Deviation (MAD)442
Skewness17.461199
Sum4737139
Variance36328531
MonotonicityNot monotonic
2025-10-31T10:11:25.673054image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
31011
 
0.4%
1508
 
0.3%
2468
 
0.3%
12007
 
0.2%
4937
 
0.2%
2727
 
0.2%
2197
 
0.2%
3947
 
0.2%
2007
 
0.2%
5167
 
0.2%
Other values (1644)2749
97.3%
ValueCountFrequency (%)
21
< 0.1%
161
< 0.1%
171
< 0.1%
191
< 0.1%
201
< 0.1%
241
< 0.1%
251
< 0.1%
272
0.1%
301
< 0.1%
321
< 0.1%
ValueCountFrequency (%)
1968441
< 0.1%
809971
< 0.1%
802631
< 0.1%
773731
< 0.1%
699931
< 0.1%
645491
< 0.1%
641241
< 0.1%
633121
< 0.1%
583431
< 0.1%
578851
< 0.1%

qtde_products
Real number (ℝ)

High correlation 

Distinct467
Distinct (%)16.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean128.19044
Minimum2
Maximum7838
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size44.1 KiB
2025-10-31T10:11:25.779200image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile10
Q134
median71
Q3142
95-th percentile393.6
Maximum7838
Range7836
Interquartile range (IQR)108

Descriptive statistics

Standard deviation275.55026
Coefficient of variation (CV)2.1495382
Kurtosis341.98106
Mean128.19044
Median Absolute Deviation (MAD)44
Skewness15.457136
Sum362138
Variance75927.945
MonotonicityNot monotonic
2025-10-31T10:11:25.877975image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2839
 
1.4%
3536
 
1.3%
2731
 
1.1%
2631
 
1.1%
2930
 
1.1%
3128
 
1.0%
2528
 
1.0%
1528
 
1.0%
1927
 
1.0%
3326
 
0.9%
Other values (457)2521
89.2%
ValueCountFrequency (%)
211
0.4%
314
0.5%
417
0.6%
516
0.6%
626
0.9%
715
0.5%
814
0.5%
920
0.7%
1019
0.7%
1123
0.8%
ValueCountFrequency (%)
78381
< 0.1%
56731
< 0.1%
50951
< 0.1%
45801
< 0.1%
26981
< 0.1%
23791
< 0.1%
20601
< 0.1%
18181
< 0.1%
16731
< 0.1%
16371
< 0.1%

avg_ticket
Real number (ℝ)

High correlation  Skewed 

Distinct1945
Distinct (%)68.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean56.841717
Minimum2.15
Maximum56157.5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size44.1 KiB
2025-10-31T10:11:25.977598image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum2.15
5-th percentile4.762
Q112.22
median17.89
Q324.98
95-th percentile88.1
Maximum56157.5
Range56155.35
Interquartile range (IQR)12.76

Descriptive statistics

Standard deviation1090.5299
Coefficient of variation (CV)19.185379
Kurtosis2490.1285
Mean56.841717
Median Absolute Deviation (MAD)6.41
Skewness48.904456
Sum160577.85
Variance1189255.4
MonotonicityNot monotonic
2025-10-31T10:11:26.072107image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
15.497
 
0.2%
17.666
 
0.2%
16.826
 
0.2%
16.536
 
0.2%
19.066
 
0.2%
16.925
 
0.2%
17.715
 
0.2%
17.135
 
0.2%
19.445
 
0.2%
105
 
0.2%
Other values (1935)2769
98.0%
ValueCountFrequency (%)
2.151
< 0.1%
2.431
< 0.1%
2.461
< 0.1%
2.511
< 0.1%
2.521
< 0.1%
2.651
< 0.1%
2.661
< 0.1%
2.711
< 0.1%
2.761
< 0.1%
2.771
< 0.1%
ValueCountFrequency (%)
56157.51
< 0.1%
13305.51
< 0.1%
4453.431
< 0.1%
1687.21
< 0.1%
1377.081
< 0.1%
952.991
< 0.1%
872.131
< 0.1%
841.021
< 0.1%
651.171
< 0.1%
6401
< 0.1%

avg_recency_days
Real number (ℝ)

High correlation  Zeros 

Distinct1218
Distinct (%)43.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean73.125451
Minimum0
Maximum366
Zeros51
Zeros (%)1.8%
Negative0
Negative (%)0.0%
Memory size44.1 KiB
2025-10-31T10:11:26.185024image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile9.5568627
Q130
median54
Q392.666667
95-th percentile212.6
Maximum366
Range366
Interquartile range (IQR)62.666667

Descriptive statistics

Standard deviation65.559622
Coefficient of variation (CV)0.89653631
Kurtosis4.1293141
Mean73.125451
Median Absolute Deviation (MAD)28.714286
Skewness1.9103653
Sum206579.4
Variance4298.0641
MonotonicityNot monotonic
2025-10-31T10:11:26.287951image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
051
 
1.8%
7020
 
0.7%
3120
 
0.7%
1416
 
0.6%
2116
 
0.6%
4615
 
0.5%
4215
 
0.5%
5515
 
0.5%
4914
 
0.5%
2513
 
0.5%
Other values (1208)2630
93.1%
ValueCountFrequency (%)
051
1.8%
0.03030303031
 
< 0.1%
0.21
 
< 0.1%
0.33333333331
 
< 0.1%
0.85714285711
 
< 0.1%
18
 
0.3%
1.51
 
< 0.1%
1.8195121951
 
< 0.1%
1.8787878791
 
< 0.1%
23
 
0.1%
ValueCountFrequency (%)
3661
 
< 0.1%
3651
 
< 0.1%
3641
 
< 0.1%
3631
 
< 0.1%
3572
0.1%
3561
 
< 0.1%
3552
0.1%
3521
 
< 0.1%
3512
0.1%
3503
0.1%

frequency
Real number (ℝ)

High correlation  Skewed 

Distinct1226
Distinct (%)43.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.087031574
Minimum0.0054495913
Maximum17
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size44.1 KiB
2025-10-31T10:11:26.381791image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0.0054495913
5-th percentile0.0088183697
Q10.015873016
median0.024793388
Q30.043478261
95-th percentile0.14935245
Maximum17
Range16.99455
Interquartile range (IQR)0.027605245

Descriptive statistics

Standard deviation0.436269
Coefficient of variation (CV)5.012767
Kurtosis810.59065
Mean0.087031574
Median Absolute Deviation (MAD)0.011000285
Skewness22.868717
Sum245.8642
Variance0.19033064
MonotonicityNot monotonic
2025-10-31T10:11:26.478090image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
247
 
1.7%
0.062518
 
0.6%
0.0277777777817
 
0.6%
0.0238095238116
 
0.6%
0.0909090909115
 
0.5%
0.0833333333315
 
0.5%
0.0294117647114
 
0.5%
0.0344827586214
 
0.5%
0.0256410256413
 
0.5%
0.0212765957413
 
0.5%
Other values (1216)2643
93.6%
ValueCountFrequency (%)
0.0054495912811
 
< 0.1%
0.0054644808741
 
< 0.1%
0.0054794520551
 
< 0.1%
0.0054945054951
 
< 0.1%
0.0055865921792
0.1%
0.0056022408961
 
< 0.1%
0.0056179775282
0.1%
0.005665722381
 
< 0.1%
0.0056818181822
0.1%
0.0056980056983
0.1%
ValueCountFrequency (%)
171
 
< 0.1%
41
 
< 0.1%
35
 
0.2%
247
1.7%
1.1428571431
 
< 0.1%
18
 
0.3%
0.751
 
< 0.1%
0.66666666673
 
0.1%
0.5508021391
 
< 0.1%
0.53351206431
 
< 0.1%

qtde_returns
Real number (ℝ)

Skewed  Zeros 

Distinct205
Distinct (%)7.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean63.067611
Minimum0
Maximum80995
Zeros1524
Zeros (%)53.9%
Negative0
Negative (%)0.0%
Memory size44.1 KiB
2025-10-31T10:11:26.569897image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q38
95-th percentile94.8
Maximum80995
Range80995
Interquartile range (IQR)8

Descriptive statistics

Standard deviation1550.2251
Coefficient of variation (CV)24.580368
Kurtosis2633.7647
Mean63.067611
Median Absolute Deviation (MAD)0
Skewness50.559909
Sum178166
Variance2403197.8
MonotonicityNot monotonic
2025-10-31T10:11:26.673064image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
01524
53.9%
1131
 
4.6%
2118
 
4.2%
382
 
2.9%
472
 
2.5%
663
 
2.2%
556
 
2.0%
1246
 
1.6%
839
 
1.4%
738
 
1.3%
Other values (195)656
23.2%
ValueCountFrequency (%)
01524
53.9%
1131
 
4.6%
2118
 
4.2%
382
 
2.9%
472
 
2.5%
556
 
2.0%
663
 
2.2%
738
 
1.3%
839
 
1.4%
938
 
1.3%
ValueCountFrequency (%)
809951
< 0.1%
90141
< 0.1%
80041
< 0.1%
44271
< 0.1%
37681
< 0.1%
33321
< 0.1%
28781
< 0.1%
20221
< 0.1%
20121
< 0.1%
17761
< 0.1%

basket_size
Real number (ℝ)

High correlation  Skewed 

Distinct1950
Distinct (%)69.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean244.69986
Minimum1
Maximum40498.5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size44.1 KiB
2025-10-31T10:11:26.769676image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile44.044
Q1102
median171.08
Q3277
95-th percentile586.9
Maximum40498.5
Range40497.5
Interquartile range (IQR)175

Descriptive statistics

Standard deviation801.56855
Coefficient of variation (CV)3.2757214
Kurtosis2255.2607
Mean244.69986
Median Absolute Deviation (MAD)81.42
Skewness45.139506
Sum691277.1
Variance642512.15
MonotonicityNot monotonic
2025-10-31T10:11:26.883827image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10011
 
0.4%
869
 
0.3%
758
 
0.3%
608
 
0.3%
2087
 
0.2%
737
 
0.2%
1977
 
0.2%
1367
 
0.2%
1057
 
0.2%
827
 
0.2%
Other values (1940)2747
97.2%
ValueCountFrequency (%)
11
< 0.1%
3.331
< 0.1%
5.331
< 0.1%
5.671
< 0.1%
6.141
< 0.1%
7.51
< 0.1%
91
< 0.1%
9.51
< 0.1%
111
< 0.1%
11.881
< 0.1%
ValueCountFrequency (%)
40498.51
< 0.1%
6009.331
< 0.1%
3868.651
< 0.1%
28801
< 0.1%
2733.941
< 0.1%
2518.771
< 0.1%
2160.331
< 0.1%
2082.231
< 0.1%
20001
< 0.1%
1903.51
< 0.1%

u_basket_size
Real number (ℝ)

High correlation 

Distinct982
Distinct (%)34.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean22.101989
Minimum1
Maximum299.71
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size44.1 KiB
2025-10-31T10:11:26.993601image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile3.382
Q110.09
median17.25
Q328
95-th percentile56.664
Maximum299.71
Range298.71
Interquartile range (IQR)17.91

Descriptive statistics

Standard deviation18.851079
Coefficient of variation (CV)0.85291323
Kurtosis23.858004
Mean22.101989
Median Absolute Deviation (MAD)8.25
Skewness3.1328747
Sum62438.12
Variance355.36318
MonotonicityNot monotonic
2025-10-31T10:11:27.116628image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1345
 
1.6%
1431
 
1.1%
1131
 
1.1%
927
 
1.0%
7.527
 
1.0%
127
 
1.0%
17.526
 
0.9%
10.526
 
0.9%
15.524
 
0.8%
1224
 
0.8%
Other values (972)2537
89.8%
ValueCountFrequency (%)
127
1.0%
1.21
 
< 0.1%
1.251
 
< 0.1%
1.332
 
0.1%
1.58
 
0.3%
1.572
 
0.1%
1.674
 
0.1%
1.831
 
< 0.1%
222
0.8%
2.051
 
< 0.1%
ValueCountFrequency (%)
299.711
< 0.1%
203.51
< 0.1%
1451
< 0.1%
136.121
< 0.1%
135.51
< 0.1%
1221
< 0.1%
1181
< 0.1%
1141
< 0.1%
110.331
< 0.1%
1101
< 0.1%

Interactions

2025-10-31T10:11:23.062061image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-31T10:11:10.341339image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-31T10:11:11.345701image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-31T10:11:12.699047image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-31T10:11:13.917505image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-31T10:11:15.070213image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-31T10:11:16.213835image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-31T10:11:17.389274image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-31T10:11:18.676705image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-31T10:11:19.738979image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-31T10:11:20.794922image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-31T10:11:21.946222image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-31T10:11:23.144987image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-31T10:11:10.417913image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-31T10:11:11.439771image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-31T10:11:12.796972image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-31T10:11:14.001039image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-31T10:11:15.166589image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-31T10:11:16.309492image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-31T10:11:17.467990image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-31T10:11:18.760956image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-31T10:11:19.823871image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-31T10:11:20.889949image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-31T10:11:22.046332image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-31T10:11:23.220073image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-31T10:11:10.491201image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-31T10:11:11.544423image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-31T10:11:12.945485image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-31T10:11:14.094227image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-31T10:11:15.259378image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-31T10:11:16.394166image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-31T10:11:17.552755image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-31T10:11:18.840963image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-31T10:11:19.908611image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-31T10:11:20.985029image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-31T10:11:22.134771image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-31T10:11:23.299802image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-31T10:11:10.562038image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-31T10:11:11.641369image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-31T10:11:13.042298image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-31T10:11:14.195170image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-31T10:11:15.348592image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-31T10:11:16.486242image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-31T10:11:17.646184image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-31T10:11:18.927281image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-31T10:11:19.995970image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-31T10:11:21.076507image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-31T10:11:22.235162image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2025-10-31T10:11:10.632407image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-31T10:11:11.731415image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2025-10-31T10:11:21.445927image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-31T10:11:22.610230image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-31T10:11:23.739528image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-31T10:11:11.003710image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-31T10:11:12.345631image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-31T10:11:13.530108image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2025-10-31T10:11:15.826188image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-31T10:11:16.984091image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-31T10:11:18.331564image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-31T10:11:19.368994image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-31T10:11:20.429588image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-31T10:11:21.563915image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-31T10:11:22.698800image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-31T10:11:23.823150image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-31T10:11:11.093197image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-31T10:11:12.433743image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-31T10:11:13.628154image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-31T10:11:14.780725image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-31T10:11:15.922360image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-31T10:11:17.079376image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-31T10:11:18.412449image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-31T10:11:19.461165image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-31T10:11:20.522441image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-31T10:11:21.662939image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-31T10:11:22.786006image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-31T10:11:24.240206image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-31T10:11:11.178328image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-31T10:11:12.526902image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-31T10:11:13.738789image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-31T10:11:14.879379image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-31T10:11:16.017899image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-31T10:11:17.182217image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-31T10:11:18.502902image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-31T10:11:19.553128image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-31T10:11:20.619711image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-31T10:11:21.757280image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-31T10:11:22.880449image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-31T10:11:24.334585image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-31T10:11:11.262389image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-31T10:11:12.617488image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-31T10:11:13.837693image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-31T10:11:14.974308image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-31T10:11:16.117591image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-31T10:11:17.294588image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-31T10:11:18.592145image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-31T10:11:19.656087image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-31T10:11:20.712940image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-31T10:11:21.850427image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-31T10:11:22.969801image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Correlations

2025-10-31T10:11:27.261173image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
avg_recency_daysavg_ticketbasket_sizecustomer_idfrequencygross_revenueqtde_invoicesqtde_itensqtde_productsqtde_returnsrecency_daysu_basket_size
avg_recency_days1.000-0.068-0.026-0.031-0.971-0.341-0.453-0.316-0.287-0.2150.1860.061
avg_ticket-0.0681.0000.203-0.1460.0620.2840.0990.203-0.3750.1970.031-0.623
basket_size-0.0260.2031.000-0.1220.0070.6040.1330.7640.4050.212-0.1120.430
customer_id-0.031-0.146-0.1221.0000.027-0.0940.003-0.0850.007-0.0620.0140.006
frequency-0.9710.0620.0070.0271.0000.2130.2720.1980.1700.151-0.095-0.071
gross_revenue-0.3410.2840.604-0.0940.2131.0000.7620.9200.7180.464-0.3800.278
qtde_invoices-0.4530.0990.1330.0030.2720.7621.0000.7040.6590.430-0.4520.019
qtde_itens-0.3160.2030.764-0.0850.1980.9200.7041.0000.7060.426-0.3730.310
qtde_products-0.287-0.3750.4050.0070.1700.7180.6590.7061.0000.326-0.3970.723
qtde_returns-0.2150.1970.212-0.0620.1510.4640.4300.4260.3261.000-0.1900.021
recency_days0.1860.031-0.1120.014-0.095-0.380-0.452-0.373-0.397-0.1901.000-0.110
u_basket_size0.061-0.6230.4300.006-0.0710.2780.0190.3100.7230.021-0.1101.000

Missing values

2025-10-31T10:11:24.469501image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
A simple visualization of nullity by column.
2025-10-31T10:11:24.598231image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

customer_idgross_revenuerecency_daysqtde_invoicesqtde_itensqtde_productsavg_ticketavg_recency_daysfrequencyqtde_returnsbasket_sizeu_basket_size
0178505391.21372.034.01733.0297.018.150.03030317.00000040.050.978.74
1130473232.5956.09.01390.0171.018.9039.6250000.02830235.0154.4419.00
2125836705.382.015.05028.0232.028.9026.5000000.04032350.0335.2015.47
313748948.2595.05.0439.028.033.8769.5000000.0179210.087.805.60
415100876.00333.03.080.03.0292.0020.0000000.07317122.026.671.00
5152914623.3025.014.02102.0102.045.3326.7692310.04011529.0150.147.29
6146885630.877.021.03621.0327.017.2218.3000000.057221399.0172.4315.57
7178095411.9116.012.02057.061.088.7232.4545450.03352041.0171.425.08
81531160767.900.091.038194.02379.025.544.1444440.243316474.0419.7126.14
9160982005.6387.07.0613.067.029.9347.6666670.0243900.087.579.57
customer_idgross_revenuerecency_daysqtde_invoicesqtde_itensqtde_productsavg_ticketavg_recency_daysfrequencyqtde_returnsbasket_sizeu_basket_size
563817468137.0010.02.0116.05.027.404.0000000.4000000.058.002.5
564913596697.045.02.0406.0166.04.207.0000000.2500000.0203.0083.0
5655148931237.859.02.0799.073.016.962.0000000.6666670.0399.5036.5
565717852114.3411.02.053.024.04.760.0000002.0000000.026.5012.0
567417772182.7710.02.058.053.03.450.0000002.0000000.029.0026.5
568014126706.137.03.0508.015.047.081.5000000.75000050.0169.335.0
568116479300.8310.02.0102.035.08.600.0000002.0000000.051.0017.5
5686135211092.391.03.0733.0435.02.514.5000000.3000000.0244.33145.0
569615060301.848.04.0262.0120.02.520.3333332.0000000.065.5030.0
57661600012393.702.03.05110.09.01377.080.0000003.0000000.01703.333.0